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Reconfigurable Neuromorphic Computing System with Memristor-Based Synapse Design

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Abstract

Conventional CMOS technology is slowly approaching its physical limitations and researchers are increasingly utilizing nanotechnology to both extend CMOS capabilities and to explore potential replacements. Novel memristive systems continue to attract growing attention since their reported physical realization by HP in 2008. Unique characteristics like non-volatility, re-configurability, and analog storage properties make memristors a very promising candidate for the realization of artificial neural systems. In this work, we propose a memristor-based design of bidirectional transmission excitation/inhibition synapses and implement a neuromorphic computing system based on our proposed synapse designs. The robustness of our system is also evaluated by considering the actual manufacturing variability with emphasis on process variation.

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Acknowledgments

This work is supported by U. S. Air Force Research Lab (AFRL) Grant FA8750-11-1-0271 and National Science Foundation (NSF) Grants ECCS-1202225 and CNS-1253424 (CAREER).

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Authors and Affiliations

  1. ECE Department, University of Pittsburgh, Pittsburgh, PA, USA

    Beiye Liu & Yiran Chen

  2. U.S. Air Force Research Laboratory, Rome, NY, USA

    Bryant Wysocki

  3. Texas A&M University at Qatar, Doha, Qatar

    Tingwen Huang

Authors
  1. Beiye Liu
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  2. Yiran Chen
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  3. Bryant Wysocki
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  4. Tingwen Huang
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Corresponding author

Correspondence to Beiye Liu.

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Liu, B., Chen, Y., Wysocki, B. et al. Reconfigurable Neuromorphic Computing System with Memristor-Based Synapse Design. Neural Process Lett 41, 159–167 (2015). https://doi.org/10.1007/s11063-013-9315-8

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  • DOI: https://doi.org/10.1007/s11063-013-9315-8

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